Compare models
Put up to 4 models beside each other — token prices, context windows, capabilities and provider, from the same catalogue the model pages read.
| Attribute | GPT-6 Luna Progpt-6-luna-pro | Transcribe 1 Protranscribe-1-pro | Grok 4.20 Multi-Agentgrok-4.20-multi-agent |
|---|---|---|---|
| Pricing | |||
| Input | $0.10 / 1M | — Not priced per input token | $2.00 / 1M |
| Output | $0.50 / 1M | — Not priced per output token | $6.00 / 1M |
| Cache Write (5m) | $0.10 / 1M | Not applicable | $2.00 / 1M |
| Cache Write (1h) | $0.10 / 1M | Not applicable | $2.00 / 1M |
| Cache Read | $0.10 / 1M | Not applicable | $2.00 / 1M |
| Web Search | $0 / 1M | $0 / 1M | $0 / 1M |
| Context | |||
| Max context | 1.1M | N/A | 2M |
| Max output | N/A | N/A | N/A |
| Capabilities | |||
| Vision | Yes | No | Yes |
| Function Calling | Yes | No | Yes |
| JSON Mode | Yes | No | Yes |
| Streaming | Yes | No | Yes |
| Catalogue | |||
| Provider | OpenAI | Fish Audio | xAI |
| Category | chat | voice | chat |
| Charge type | Pay As You Go | Pay As You Go | Pay As You Go |
| Released | — | 2026-09-24 | — |
| Description | |||
| Summary | GPT-6 Luna Pro uses the same underlying model as GPT-6 Luna, but runs with reasoning.mode set to pro for higher-quality responses on complex tasks. It combines Luna's speed and cost efficiency with deeper reasoning and more reliable multi-step execution, making it well suited for advanced coding, computer use, and agentic workflows where higher solution quality is needed without moving to a larger GPT-6 tier. | Fish Audio Transcribe 1 Pro speech-to-text with speaker labels; transcripts include speaker tags such as <|speaker:0|>. Billed per second of audio. | Grok 4.20 Multi-Agent is a specialized variant of xAI's Grok 4.20 designed for collaborative, agent-based workflows. It enables multiple agents to operate in parallel, coordinating tool use and synthesizing information to handle complex, multi-step tasks. Optimized for deep research and large-scale problem solving, the model supports configurable reasoning effort: 4 agents for low/medium settings and up to 16 agents for high/xhigh settings, enabling scalable parallel reasoning and execution. |